Can glm 4 9b chat 1m run on RTX A4500 20GB?

YES — Runs Great

C52Usable

Estimated from fit model

glm 4 9b chat 1m needs ~9.7 GB VRAM. RTX A4500 20GB has 20.0 GB. With Q4_K_M quantization, expect ~91 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 9.7 GB, 90.9 tok/s, Runs well

9.7 GB required20.0 GB available

49% VRAM used

Fit status

Runs well

Decode

90.9 tok/s

TTFT

2129 ms

Safe context

172K

Memory

9.7 GB / 20.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsglm 4 9b chat 1m on RTX A4500 20GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 90.9 tok/s decode · 2.1s TTFT (warm) · 227 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	90.9 tok/s	1161 ms	172K
Coding	C	Runs well	90.9 tok/s	2129 ms	172K
Agentic Coding	C	Runs well	90.9 tok/s	3097 ms	172K
Reasoning	C	Runs well	90.9 tok/s	2516 ms	172K
RAG	C	Runs well	90.9 tok/s	3871 ms	172K

Quantization options

How glm 4 9b chat 1m (9B params) fits at each quantization level on RTX A4500 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C46
Q3_K_S	3	4.4 GB	Low	C46
NVFP4	4	5.0 GB	Medium	C47
Q4_K_M	4	5.5 GB	Medium	C47
Q5_K_M	5	6.5 GB	High	C48
Q6_K	6	7.4 GB	High	C49
Q8_0Best for your GPU	8	9.6 GB	Very High	C50
F16	16	18.5 GB	Maximum	F0

Get started

Copy-paste commands to run glm 4 9b chat 1m on your machine.

Run

lms load hf-bartowski--glm-4-9b-chat-1m-gguf && lms server start

Frequently asked questions

Can RTX A4500 20GB run glm 4 9b chat 1m?

Yes, RTX A4500 20GB can run glm 4 9b chat 1m with a C grade (Runs well). Expected decode speed: 90.9 tok/s.

How much VRAM does glm 4 9b chat 1m need?

glm 4 9b chat 1m (9B parameters) requires approximately 9.7 GB of memory with Q4_K_M quantization.

What is the best quantization for glm 4 9b chat 1m?

The recommended quantization for glm 4 9b chat 1m is Q4_K_M, which balances quality and memory efficiency.

What speed will glm 4 9b chat 1m run at on RTX A4500 20GB?

On RTX A4500 20GB, glm 4 9b chat 1m achieves approximately 90.9 tokens per second decode speed with a time-to-first-token of 2129ms using Q4_K_M quantization.

Can RTX A4500 20GB run glm 4 9b chat 1m for coding?

For coding workloads, glm 4 9b chat 1m on RTX A4500 20GB receives a C grade with 90.9 tok/s and 172K context.

What context window can glm 4 9b chat 1m use on RTX A4500 20GB?

On RTX A4500 20GB, glm 4 9b chat 1m can safely use up to 172K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX A4500 20GB See all hardware for glm 4 9b chat 1m

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<iframe src="https://willitrunai.com/embed/hf-bartowski--glm-4-9b-chat-1m-gguf-on-rtx-a4500-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

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